How Politicians and Governments Could Benefit from Statistical Analyses

Bruno Scibilia | 22 September, 2014

Topics: Design of Experiments - DOE, Government, Hypothesis Testing, Lean Six Sigma, Services, Statistics in the News, Healthcare, Data Analysis, Statistics

Using statistical techniques to optimize manufacturing processes is quite common now, but using the same approach on social topics is still an innovative approach. For example, if our objective is to improve student academic performances, should we increase teachers wages or would it be better to reduce the number of students in a class?

Many social topics (the effect of increasing the minimum wage on employment, etc.) generate long and passionate discussions in the media and in politics. People express very different and subjective points of views according to political/ideological opinions and varied ways of thinking.

Hypothesis Testing in the Policy Realm

Social experimentation and data analysis can provide a firmer ground on which we can base more objective decisions.

The objective is to investigate the effects of a policy intervention and to test specific hypotheses. In these social experiments “randomization” is a key element. If one policy option is tested in, say, the Netherlands, and another policy option is tested in France, the experimenter will never be in a position to fully understand whether a difference in outcomes is due to the intervention itself or to the many other differences between these two countries.

It would clearly be preferable to test the two approaches in different regions of France and of the Netherlands, for example, and assign the policy intervention in a random way to a “treatment” group (individuals who receive it) and a “comparison” group (individuals who do not receive it).

At the beginning of the study, the “treatment” and the “control” groups should be as similar as possible to prevent any systematic previous bias. The objective is not to “observe” differences but to identify the actual causal effects.

Designed Experiment Techniques

Other techniques that are often used in designed experiments (DOEs) may also be useful in this context, such as blocking and balancing. In my example, France and the Netherlands might be considered as a blocking factor (an external extra factor which the experimenter cannot control), and the tests should be “balanced” across blocks so that the treatment effect estimates are not biased and the blocking effects of the countries are neutralized. Other potential blocking factors in policy studies might be urban versus rural regions, or females versus males.

Examples of Policy Experiments

Data analysis and statistics have been used to inform several important policy debates around the world over the past few years.  Here are a few examples:

- In Kenya, a social experiment showed that neither hiring extra teachers to reduce class sizes in schools nor providing more textbooks to pupils had much effect on academic performances. A surprising finding of this study was that deworming (intestinal worms) programs were very effective in decreasing child absenteeism.

- In the U.S, a full factorial design (DOE) was used to assess the effectiveness of commitment contracts. The objective of these contracts was to encourage individuals to exercise more in order to reduce health risks and prevent obesity. The effects of different factors such as duration of the physical exercises, their frequency and financial stakes were studied. The outcome was the likelihood of accepting such a contract.

- Different strategies to quit smoking based on commitment contracts have been tested using a randomized experimental approach.

- In France, a social experiment was conducted to compare different job-counselling strategies for placing young unemployed people. The studied outcome was the probability to find a job.


Experiments make it possible to vary one factor at a time, but a more effective approach is to modify several factors for each test using proper designs of experiments. Expertise in setting up randomized field experiments to test economic hypotheses is clearly a key factor.

Experimental results are often surprising, therefore experimentation and data analysis are potentially new and powerful tools in the arsenal of politicians and governments.

Here are sources of more information about the examples I've mentioned :

Miguel, Edward and Michael Kremer (2004). “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities,” Econometrica, Volume72 (1), pp. 159-217.

Gine, Xavier, Dean Karlan and Jonathan Zinman (2008). “Put Your Money Where Your Butt Is: A Commitment Savings Account for Smoking Cessation,” MIMEO, Yale University.

Using Nudges in Exercise Commitment Contracts :